| Autor: |
Lu Zhang, Guy Fagherazzi, Aurélie Fischer, Gloria Aguayo, Abir Elbéji, Eduardo Higa, Vladimir Despotovic, Petr V Nazarov |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
BMJ Open, Vol 12, Iss 11 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2044-6055 |
| DOI: |
10.1136/bmjopen-2022-062463 |
| Popis: |
Objective To develop a vocal biomarker for fatigue monitoring in people with COVID-19.Design Prospective cohort study.Setting Predi-COVID data between May 2020 and May 2021.Participants A total of 1772 voice recordings were used to train an AI-based algorithm to predict fatigue, stratified by gender and smartphone’s operating system (Android/iOS). The recordings were collected from 296 participants tracked for 2 weeks following SARS-CoV-2 infection.Primary and secondary outcome measures Four machine learning algorithms (logistic regression, k-nearest neighbours, support vector machine and soft voting classifier) were used to train and derive the fatigue vocal biomarker. The models were evaluated based on the following metrics: area under the curve (AUC), accuracy, F1-score, precision and recall. The Brier score was also used to evaluate the models’ calibrations.Results The final study population included 56% of women and had a mean (±SD) age of 40 (±13) years. Women were more likely to report fatigue (p |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
